Advanced Lane Finding Project¶
The goals / steps of this project are the following:
- Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
- Apply a distortion correction to raw images.
- Use color transforms, gradients, etc., to create a thresholded binary image.
- Apply a perspective transform to rectify binary image ("birds-eye view").
- Detect lane pixels and fit to find the lane boundary.
- Determine the curvature of the lane and vehicle position with respect to center.
- Warp the detected lane boundaries back onto the original image.
- Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.
- Modularized the previous code
Camera Calibration¶
Step1: I'll compute the camera calibration using chessboard images¶
In [1]:
import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
%matplotlib qt
Find the Chessboard Corners¶
In [3]:
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((6*9,3), np.float32)
objp[:,:2] = np.mgrid[0:9,0:6].T.reshape(-1,2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.
# Make a list of calibration images
images = glob.glob('./camera_cal/calibration*.jpg')
# Plot the result
fig, axs = plt.subplots(5, 4, figsize=(24, 10)) #axs[20]
axs = axs.flatten()
# fig.subplots_adjust(left=0.2, bottom=0.2, right=0.8, top=0.8, hspace=0.2, wspace=0.1)
fig.tight_layout()
# Step through the list and search for chessboard corners
serial_number = 0
for i, fname in enumerate(images):
img = cv2.imread(fname)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# Find the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (9,6),None)
# If found, add object points, image points
if ret == True:
objpoints.append(objp)
imgpoints.append(corners)
# Draw and display the corners
img = cv2.drawChessboardCorners(img, (9,6), corners, ret)
axs[i].axis("off")
axs[i].imshow(img)
axs[i].set_title(str(i), fontsize=10)
Compute the camera calibration¶
In [4]:
import pickle
# Test undistortion on an image
img = cv2.imread('./camera_cal/calibration10.jpg')
img_size = (img.shape[1], img.shape[0])
# Do camera calibration given object points and image points
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size,None,None)
dst = cv2.undistort(img, mtx, dist, None, mtx)
cv2.imwrite('./camera_cal/test_undist_10.jpg',dst)
# Save the camera calibration result for later use (we won't worry about rvecs / tvecs)
dist_pickle = {}
dist_pickle["mtx"] = mtx
dist_pickle["dist"] = dist
dist_pickle["ret"] = ret
dist_pickle["rvecs"] = rvecs
dist_pickle["tvecs"] = tvecs
pickle.dump( dist_pickle, open( "./camera_cal/wide_dist_pickle.p", "wb" ) )
#dst = cv2.cvtColor(dst, cv2.COLOR_BGR2RGB)
# Visualize undistortion
fig, axs = plt.subplots(1, 2, figsize=(20,10))
axs[0].imshow(img)
axs[0].set_title('Original Image', fontsize=30)
axs[1].imshow(dst)
axs[1].set_title('Undistorted Image', fontsize=30)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
Step2: I'll apply a distortion correction to raw images.¶
In [5]:
pkl_file = open( "./camera_cal/wide_dist_pickle.p", "rb" )
dist_pickle = pickle.load( pkl_file )
mtx = dist_pickle["mtx"]
dist = dist_pickle["dist"]
# print('mtx=', mtx)
# print('dis=',dist)
# dst = cv2.undistort(img, mtx, dist, None, mtx)
# cv2.imwrite('calibration_wide/test_undist.jpg',dst)
# Plot the result
fig, axs = plt.subplots(5, 4, figsize=(24, 10)) #axs[20]
axs = axs.flatten()
# fig.subplots_adjust(left=0.2, bottom=0.2, right=0.8, top=0.8, hspace=0.2, wspace=0.1)
fig.tight_layout()
# Step through the list and search for chessboard corners
for i, fname in enumerate(images):
img = cv2.imread(fname)
dst = cv2.undistort(img, mtx, dist, None, mtx)
# cv2.imwrite('calibration_wide/test_undist.jpg',dst)
axs[i].axis("off")
axs[i].imshow(dst)
axs[i].set_title(str(i), fontsize=10)
In [7]:
import os
images_name = os.listdir('./test_images')
# print(images_name)
for i, fname in enumerate(images_name):
img = cv2.imread('./test_images/' + fname)
dst = cv2.undistort(img, mtx, dist, None, mtx)
cv2.imwrite('./output_images/output_images_undistort/undistort_' + fname, dst) #distortion correction of the picture, then save
img = cv2.imread('./test_images/test1.jpg')
undis_img = cv2.imread('./output_images/output_images_undistort/undistort_test1.jpg')
# Visualize undistortion of test images
fig, axs = plt.subplots(1, 2, figsize=(20,10))
axs[0].imshow(img[:,:,::-1])
axs[0].set_title('Original Image', fontsize=30)
axs[1].imshow(undis_img[:,:,::-1])
axs[1].set_title('Undistorted Image', fontsize=30)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
Step3: I'll use color transforms, gradients, etc., to create a thresholded binary image.¶
In [8]:
# Read the undistort_straight_lines1.jpg
img = cv2.imread('./output_images/output_images_undistort/undistort_straight_lines1.jpg')
# Define a function that thresholds the S-channel of HLS
def binary_select(image, s_thresh=(170, 255), sx_thresh=(20, 100)):
hls = cv2.cvtColor(image, cv2.COLOR_RGB2HLS) #Convert to HLS color space
s_channel = hls[:,:,2] #Apply a threshold to the S channel
# Threshold color channel
s_binary = np.zeros_like(s_channel)
s_binary[(s_channel >= s_thresh[0]) & (s_channel <= s_thresh[1])] = 255
# Sobel x
sobelx = cv2.Sobel(s_channel, cv2.CV_64F, 1, 0) # Take the derivative in x
abs_sobelx = np.absolute(sobelx) # Absolute x derivative to accentuate lines away from horizontal
scaled_sobel = np.uint8(255*abs_sobelx/np.max(abs_sobelx))
# Threshold x gradient
sxbinary = np.zeros_like(scaled_sobel)
sxbinary[(scaled_sobel >= sx_thresh[0]) & (scaled_sobel <= sx_thresh[1])] = 255
#the threshold result of combine s_binary and sxbinary
binary_output = np.zeros_like(s_channel)
binary_output[(s_binary >= 255) & (sxbinary >= 255)] = 255
return s_channel, s_binary, sxbinary, binary_output
s_channel, s_binary, sxbinary, binary_output = binary_select(img, s_thresh=(90, 255), sx_thresh=(10, 150))
# Plot the result
fig, axs = plt.subplots(2, 3, figsize=(24, 9))
fig.tight_layout()
axs = axs.flatten()
axs[0].imshow(img[:,:,::-1])
axs[0].set_title('Original Image', fontsize=20)
axs[1].imshow(s_channel, cmap='gray')
axs[1].set_title('Thresholded S', fontsize=20)
axs[2].imshow(s_binary, cmap='gray')
axs[2].set_title('s_binary', fontsize=20)
axs[3].imshow(sxbinary, cmap='gray')
axs[3].set_title('sxbinary', fontsize=20)
axs[4].imshow(binary_output, cmap='gray')
axs[4].set_title('binary_output', fontsize=20)
plt.subplots_adjust(left=0., right=1, top=1.1, bottom=0.)
import os
images_name = os.listdir('./output_images/output_images_undistort')
# print(images_name)
for i, fname in enumerate(images_name):
if fname.split('.')[-1] == 'jpg':
img = cv2.imread('./output_images/output_images_undistort/' + fname)
s_channel, s_binary, sxbinary, binary_output = binary_select(img, s_thresh=(90, 255), sx_thresh=(10, 150))
cv2.imwrite('./output_images/output_images_binary_undistort/binary_' + fname, binary_output) #create a thresholded binary image, then save?
Step4: I'll apply a perspective transform to rectify binary image ("birds-eye view").¶
In [26]:
# Read in the saved camera matrix and distortion coefficients
pkl_file = open("./camera_cal/wide_dist_pickle.p", "rb")
dist_pickle = pickle.load(pkl_file)
mtx = dist_pickle["mtx"]
dist = dist_pickle["dist"]
# the function of "birds-eye view"
def birds_eye_warp(img, src, dst): #src: 4 source points / dst: 4 destination points
h,w = img.shape[:2]
# use cv2.getPerspectiveTransform() to get M, the transform matrix, and Minv, the inverse
M = cv2.getPerspectiveTransform(src, dst)
Minv = cv2.getPerspectiveTransform(dst, src)
# use cv2.warpPerspective() to warp your image to a top-down view
warped = cv2.warpPerspective(img, M, (w,h), flags=cv2.INTER_LINEAR)
return warped, M, Minv
# Read in an image
img = cv2.imread('./output_images/output_images_undistort/undistort_straight_lines1.jpg')
h,w = img.shape[:2]
# define source and destination points for transform
src = np.float32([[575,464], [707,464],
[1049,682],[258,682]])
dst = np.float32([[150,0],[w-150,0],
[w-150,h],[150,h]])
# #test which are the best points in src and dst
x_src = []
y_src = []
x_dst = []
y_dst = []
x_vertices_quad = []
y_vertices_quad = []
for i in range(4):
x_src.append(src[i][0])
y_src.append(src[i][1])
x_src.append(src[0][0])
y_src.append(src[0][1])
for i in range(4):
x_dst.append(dst[i][0])
y_dst.append(dst[i][1])
x_dst.append(dst[0][0])
y_dst.append(dst[0][1])
top_down, perspective_M, perspective_Minv = birds_eye_warp(img, src, dst)
fig, axs = plt.subplots(1, 2, figsize=(24, 9))
fig.tight_layout()
axs = axs.flatten()
axs[0].imshow(img[:,:,::-1])
axs[0].set_title('Original Image', fontsize=20)
axs[1].imshow(top_down[:,:,::-1])
axs[1].set_title('Undistorted and Warped Image', fontsize=20)
axs[1].plot(x_dst, y_dst, 'b--', lw=4)
axs[0].plot(x_src, y_src, 'g--', lw=4)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [12]:
images_name = os.listdir('./output_images/output_images_undistort')
for i, fname in enumerate(images_name):
if fname.split('.')[-1] == 'jpg':
img = cv2.imread('./output_images/output_images_undistort/' + fname)
top_down, perspective_M, perspective_Minv = birds_eye_warp(img, src, dst)
cv2.imwrite('./output_images/output_images_bird_eye/birds_eye_' + fname, top_down) #create a thresholded binary image, then save?
images_name = os.listdir('./output_images/output_images_binary_undistort')
for i, fname in enumerate(images_name):
if fname.split('.')[-1] == 'jpg':
img = cv2.imread('./output_images/output_images_binary_undistort/' + fname)
top_down, perspective_M, perspective_Minv = birds_eye_warp(img, src, dst)
cv2.imwrite('./output_images/output_images_binary_undistort_bird_eye/birds_eye_binary' + fname, top_down) #create a thresholded binary image, then save?
# output_images_binary_undistort_bird_eye
In [14]:
import numpy as np
import matplotlib.pyplot as plt
import cv2
# Load our image
binary_warped = cv2.imread('./output_images/output_images_binary_undistort_bird_eye/birds_eye_binarybinary_undistort_straight_lines1.jpg',0)
binary_warped = binary_warped/255
def hist(img):
# TO-DO: Grab only the bottom half of the image
# Lane lines are likely to be mostly vertical nearest to the car
bottom_half = None
bottom_half = img[img.shape[0]//2:,]
# TO-DO: Sum across image pixels vertically - make sure to set `axis`
# i.e. the highest areas of vertical lines should be larger values
histogram = None
histogram = np.sum(bottom_half, axis = 0)
return histogram
# Create histogram of image binary activations
histogram = hist(binary_warped)
# Visualize the resulting histogram
plt.plot(histogram)
Out[14]:
Implement Sliding Windows and Fit a Polynomial¶
In [15]:
# Load our image - this should be a new frame since last time!
# binary_warped = mpimg.imread('warped_example.jpg')
binary_warped = cv2.imread('./output_images/output_images_binary_undistort_bird_eye/birds_eye_binarybinary_undistort_straight_lines1.jpg',0)
def find_lane_pixels(binary_warped):
# Take a histogram of the bottom half of the image
histogram = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0) #WHC
# Create an output image to draw on and visualize the result
out_img = np.dstack((binary_warped, binary_warped, binary_warped))
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]//2) #W/2
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
# HYPERPARAMETERS
# Choose the number of sliding windows
nwindows = 9
# Set the width of the windows +/- margin
margin = 100
# Set minimum number of pixels found to recenter window
minpix = 50
# Set height of windows - based on nwindows above and image shape
window_height = np.int(binary_warped.shape[0]//nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0]) #H
nonzerox = np.array(nonzero[1]) #W
# Current positions to be updated later for each window in nwindows
leftx_current = leftx_base
rightx_current = rightx_base
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# Identify the nonzero pixels in x and y within the window #
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices (previously was a list of lists of pixels)
try:
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
except ValueError:
# Avoids an error if the above is not implemented fully
pass
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
return leftx, lefty, rightx, righty, out_img
def fit_polynomial(binary_warped):
# Find our lane pixels first
leftx, lefty, rightx, righty, out_img = find_lane_pixels(binary_warped)
# Fit a second order polynomial to each using `np.polyfit`
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
try:
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
except TypeError:
# Avoids an error if `left` and `right_fit` are still none or incorrect
print('The function failed to fit a line!')
left_fitx = 1*ploty**2 + 1*ploty
right_fitx = 1*ploty**2 + 1*ploty
## Visualization ##
# Colors in the left and right lane regions
out_img[lefty, leftx] = [255, 0, 0]
out_img[righty, rightx] = [0, 0, 255]
window_img = np.zeros_like(out_img)
# Generate a polygon to illustrate the search window area
# And recast the x and y points into usable format for cv2.fillPoly()
margin = 20
line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin,
ploty])))])
line_pts = np.hstack((line_window1, line_window2))
# Draw the lane onto the warped blank image
cv2.fillPoly(window_img, np.int_([line_pts]), (0,255, 0))
result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
return result, left_fitx, right_fitx, ploty, left_fit, right_fit
result, left_fitx, right_fitx, ploty, left_fit, right_fit = fit_polynomial(binary_warped)
fig, axs = plt.subplots(1, 2, figsize=(24, 9))
fig.tight_layout()
axs = axs.flatten()
axs[0].imshow(binary_warped, cmap='gray')
axs[0].set_title('Original Image', fontsize=20)
axs[1].imshow(result)
# Plots the left and right polynomials on the lane lines
axs[1].plot(left_fitx, ploty, color='yellow')
axs[1].plot(right_fitx, ploty, color='yellow')
axs[1].set_title('Undistorted and Warped Image', fontsize=20)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
Step6: Determine the curvature of the lane and vehicle position with respect to center.¶
In [17]:
def measure_curvature_real(left_fitx, right_fitx, ploty, left_fit, right_fit):
'''
Calculates the curvature of polynomial functions in meters.
'''
# Define conversions in x and y from pixels space to meters
ym_per_pix = 16.0/720 # meters per pixel in y dimension
xm_per_pix = 3.7/1000 # meters per pixel in x dimension
leftx = left_fitx*xm_per_pix
rightx = right_fitx*xm_per_pix
ploty = ploty*ym_per_pix
left_fit_cr = np.polyfit(ploty, leftx, 2)
right_fit_cr = np.polyfit(ploty, rightx, 2)
# Define y-value where we want radius of curvature
# We'll choose the maximum y-value, corresponding to the bottom of the image
y_eval = np.max(ploty)
##### TO-DO: Implement the calculation of R_curve (radius of curvature) #####
left_curverad = ((1 + (2*left_fit_cr[0]*y_eval*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
right_curverad = ((1 + (2*right_fit_cr[0]*y_eval*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
return left_curverad, right_curverad
# Calculate the radius of curvature in meters for both lane lines
left_curverad, right_curverad = measure_curvature_real(left_fitx, right_fitx, ploty, left_fit, right_fit)
print('left_curverad =', left_curverad, 'm;', 'right_curverad=', right_curverad, 'm')
# Should see values of 533.75 and 648.16 here, if using
# the default `generate_data` function with given seed number
Step7: Warp the detected lane boundaries back onto the original image.¶
In [18]:
# print(perspective_Minv, '\n\n', perspective_M)
h, w = result.shape[:2]
print(result.shape)
warped_Minv = cv2.warpPerspective(result, perspective_Minv, (w,h), flags=cv2.INTER_LINEAR)
plt.imshow(warped_Minv)
img = cv2.imread('./output_images/output_images_undistort/undistort_straight_lines1.jpg')
img_warped_Minv = cv2.addWeighted(img, 1, warped_Minv, 1, 0)
plt.imshow(img_warped_Minv[:,:,::-1])
Out[18]:
Step8: Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.¶
In [19]:
# Calculate the position of the vehicle
leftx_int = left_fit[0]*720**2 + left_fit[1]*720 + left_fit[2]
rightx_int = right_fit[0]*720**2 + right_fit[1]*720 + right_fit[2]
center = abs(w/2 - ((rightx_int+leftx_int)/2))
plt.imshow(img_warped_Minv[:,:,::-1])
plt.text(50, 50, 'Radius of curvature = {}(m)'.format(int((left_curverad + right_curverad)/2)),
style='italic', color='white', fontsize=10)
plt.text(50, 100, 'Vehicle is {:.2f}m left of center'.format(center*3.7/700),
style='italic', color='white', fontsize=10)
Out[19]:
In [22]:
import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
from moviepy.editor import VideoFileClip
import pickle
from ipywidgets import interact, interactive, fixed
from moviepy.editor import VideoFileClip
from IPython.display import HTML
%matplotlib qt
In [23]:
# Define a class to receive the characteristics of each line detection
class Line():
def __init__(self):
# was the line detected in the last iteration?
self.detected = False
# x values of the last n fits of the line
self.recent_xfitted = []
#average x values of the fitted line over the last n iterations
self.bestx = None
#polynomial coefficients averaged over the last n iterations
self.best_fit = None
#polynomial coefficients for the most recent fit
self.current_fit = []
#radius of curvature of the line in some units
self.radius_of_curvature = None
#distance in meters of vehicle center from the line
self.line_base_pos = None
#difference in fit coefficients between last and new fits
self.diffs = np.array([0,0,0], dtype='float')
#x values for detected line pixels
self.allx = None
#y values for detected line pixels
self.ally = None
def add_fit(self, fit):
# add a found fit to the line, up to n
if fit is not None:
if self.best_fit is not None:
# if we have a best fit, see how this new fit compares
self.diffs = abs(fit-self.best_fit)
if (self.diffs[0] > 0.001 or \
self.diffs[1] > 1.0 or \
self.diffs[2] > 100.) and \
len(self.current_fit) > 0:
# bad fit! abort! abort! ... well, unless there are no fits in the current_fit queue, then we'll take it
self.detected = False
else:
self.detected = True
self.current_fit.append(fit)
if len(self.current_fit) > 5:
# throw out old fits, keep newest n
self.current_fit = self.current_fit[len(self.current_fit)-5:]
self.best_fit = np.average(self.current_fit, axis=0)
# or remove one from the history, if not found
else:
self.detected = False
if len(self.current_fit) > 0:
# throw out oldest fit
self.current_fit = self.current_fit[:len(self.current_fit)-1]
if len(self.current_fit) > 0:
# if there are still any fits in the queue, best_fit is their average
self.best_fit = np.average(self.current_fit, axis=0)
In [ ]:
# Define the complete image processing pipeline, reads raw image and returns binary image with lane lines identified
# (hopefully)
# Define a function that thresholds the L-channel of HLS
def hls_lthresh(img, thresh=(220, 255)):
# Convert to HLS color space
hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
hls_l = hls[:,:,1]
hls_l = hls_l*(255/np.max(hls_l))
# Apply a threshold to the L channel
binary_output = np.zeros_like(hls_l)
binary_output[(hls_l > thresh[0]) & (hls_l <= thresh[1])] = 1
# Return a binary image of threshold result
return binary_output
# Define a function that thresholds the S-channel of HLS
def hls_sthresh(img, thresh=(170, 255)):
# Convert to HLS color space
hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
hls_s = hls[:,:,2]
hls_s = hls_s*(255/np.max(hls_l))
# Apply a threshold to the S channel
binary_output = np.zeros_like(hls_s)
binary_output[(hls_s > thresh[0]) & (hls_s <= thresh[1])] = 1
# Return a binary image of threshold result
return binary_output
# Define a function that thresholds the B-channel of LAB
def lab_bthresh(img, thresh=(190,255)):
# Convert to LAB color space
lab = cv2.cvtColor(img, cv2.COLOR_RGB2Lab)
lab_b = lab[:,:,2]
# don't normalize if there are no yellows in the image
if np.max(lab_b) > 175:
lab_b = lab_b*(255/np.max(lab_b))
# Apply a threshold to the L channel
binary_output = np.zeros_like(lab_b)
binary_output[((lab_b > thresh[0]) & (lab_b <= thresh[1]))] = 1
return binary_output
# def abs_sobel_thresh(img, orient='x', thresh_min=0, thresh_max=255):
def abs_sobel_thresh(img, thresh=(0,255), orient='x'):
# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Apply x or y gradient with the OpenCV Sobel() function and take the absolute value
if orient == 'x':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 1, 0))
if orient == 'y':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 0, 1))
# Rescale back to 8 bit integer
scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
# Create a copy and apply the threshold
binary_output = np.zeros_like(scaled_sobel)
binary_output[(scaled_sobel > thresh[0]) & (scaled_sobel <= thresh[1])] = 1
return binary_output
# Define a function to threshold an image for a given range and Sobel kernel
def dir_threshold(img, thresh=(0,255)):
# Grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Calculate the x and y gradients
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0)#kernel = 3
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1)
# Take the absolute value of the gradient direction, apply a threshold, and create a binary image result
absgraddir = np.arctan2(np.absolute(sobely), np.absolute(sobelx))
binary_output = np.zeros_like(absgraddir)
binary_output[(absgraddir > thresh[0]) & (absgraddir <= thresh[1])] = 1
# Return the binary image
return binary_output
# combine HLS and sobelx
def binary_select(img, s_thresh=(170, 255), sx_thresh=(20, 100)):
hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS) #Convert to HLS color space
hls_s = hls[:,:,2] #Apply a threshold to the S channel
# Threshold color channel
s_binary = np.zeros_like(hls_s)
s_binary[(hls_s > s_thresh[0]) & (hls_s <= s_thresh[1])] = 1
# Sobel x
sobelx = cv2.Sobel(hls_s, cv2.CV_64F, 1, 0) # Take the derivative in x
abs_sobelx = np.absolute(sobelx) # Absolute x derivative to accentuate lines away from horizontal
scaled_sobel = np.uint8(255*abs_sobelx/np.max(abs_sobelx))
# Threshold x gradient
sxbinary = np.zeros_like(scaled_sobel)
sxbinary[(scaled_sobel > sx_thresh[0]) & (scaled_sobel <= sx_thresh[1])] = 1
#the threshold result of combine s_binary and sxbinary
binary_output = np.zeros_like(hls_s)
binary_output[(s_binary >= 1) & (sxbinary >= 1)] = 1
return binary_output
def threshold_warp(img_undistort):
# define source and destination points for transform
h,w = img_undistort.shape[:2]
src = np.float32([[575,464], [707,464],
[1049,682],[258,682]])
dst = np.float32([[150,0],[w-150,0],
[w-150,h],[150,h]])
# Perspective Transform
img_unwarp, M, Minv = unwarp(img_undistort, src, dst)
# HLS L-channel Threshold (using default parameters)
img_LThresh = hls_lthresh(img_unwarp)
# Lab B-channel Threshold (using default parameters)
img_BThresh = lab_bthresh(img_unwarp)
# Combine HLS and Lab B channel thresholds
combined = np.zeros_like(img_BThresh)
combined[(img_LThresh == 1) | (img_BThresh == 1)] = 1
return combined, Minv
def process_image(img):
# Undistort
img_undistort = undistort(img)
new_img = np.copy(img)
img_bin, Minv = threshold_warp(new_img)
# if both left and right lines were detected last frame, use polyfit_using_prev_fit, otherwise use sliding window
if not l_line.detected or not r_line.detected:
l_fit, r_fit, l_lane_inds, r_lane_inds, _ = sliding_window_polyfit(img_bin)
else:
l_fit, r_fit, l_lane_inds, r_lane_inds = polyfit_using_prev_fit(img_bin, l_line.best_fit, r_line.best_fit)
# invalidate both fits if the difference in their x-intercepts isn't around 900 px (+/- 150 px)
if l_fit is not None and r_fit is not None:
# calculate x-intercept (bottom of image, x=image_height) for fits
h = img.shape[0]
l_fit_x_int = l_fit[0]*h**2 + l_fit[1]*h + l_fit[2]
r_fit_x_int = r_fit[0]*h**2 + r_fit[1]*h + r_fit[2]
x_int_diff = abs(r_fit_x_int-l_fit_x_int)
if abs(900 - x_int_diff) > 150:
l_fit = None
r_fit = None
l_line.add_fit(l_fit)
r_line.add_fit(r_fit)
# draw the current best fit if it exists
if l_line.best_fit is not None and r_line.best_fit is not None:
img_out_lane = draw_lane(new_img, img_bin, l_line.best_fit, r_line.best_fit, Minv)
rad_l, rad_r, d_center = calc_curv_rad_and_center_dist(img_bin, l_line.best_fit, r_line.best_fit,
l_lane_inds, r_lane_inds)
img_out = draw_data(img_out_lane, (rad_l+rad_r)/2, d_center)
else:
img_out = new_img
return img_out
In [28]:
def unwarp(img, src, dst):
h,w = img.shape[:2]
# use cv2.getPerspectiveTransform() to get M, the transform matrix, and Minv, the inverse
M = cv2.getPerspectiveTransform(src, dst)
Minv = cv2.getPerspectiveTransform(dst, src)
# use cv2.warpPerspective() to warp your image to a top-down view
warped = cv2.warpPerspective(img, M, (w,h), flags=cv2.INTER_LINEAR)
return warped, M, Minv
# undistort image using camera calibration matrix from above
def undistort(img):
# Read in the saved camera matrix and distortion coefficients
pkl_file = open("./camera_cal/wide_dist_pickle.p", "rb")
dist_pickle = pickle.load(pkl_file)
mtx = dist_pickle["mtx"]
dist = dist_pickle["dist"]
undist = cv2.undistort(img, mtx, dist, None, mtx)
return undist
def plot_fit_onto_img(img, fit, plot_color):
if fit is None:
return img
new_img = np.copy(img)
h = new_img.shape[0]
ploty = np.linspace(0, h-1, h)
plotx = fit[0]*ploty**2 + fit[1]*ploty + fit[2]
pts = np.array([np.transpose(np.vstack([plotx, ploty]))])
cv2.polylines(new_img, np.int32([pts]), isClosed=False, color=plot_color, thickness=8)
return new_img
def draw_data(original_img, curv_rad, center_dist):
new_img = np.copy(original_img)
h = new_img.shape[0]
font = cv2.FONT_HERSHEY_DUPLEX
text = 'Radius of curvature = ' + '{:04.2f}'.format(curv_rad) + '(m)'
cv2.putText(new_img, text, (40,70), font, 1.5, (255,255,255), 2, cv2.LINE_AA)
direction = ''
if center_dist > 0:
direction = 'right'
elif center_dist < 0:
direction = 'left'
abs_center_dist = abs(center_dist)
text = 'Vehicle is '+'{:04.3f}'.format(abs_center_dist) + '(m) ' + direction + ' of center'
cv2.putText(new_img, text, (40,120), font, 1.5, (255,255,255), 2, cv2.LINE_AA)
return new_img
# img_out1 = draw_lane(new_img, img_bin, l_line.best_fit, r_line.best_fit, Minv)
def draw_lane(original_img, binary_img, l_fit, r_fit, Minv):
new_img = np.copy(original_img)
if l_fit is None or r_fit is None:
return original_img
# Create an image to draw the lines on
warp_zero = np.zeros_like(binary_img).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
h,w = binary_img.shape[:2]
ploty = np.linspace(0, h-1, num=h)# to cover same y-range as image
left_fitx = l_fit[0]*ploty**2 + l_fit[1]*ploty + l_fit[2]
right_fitx = r_fit[0]*ploty**2 + r_fit[1]*ploty + r_fit[2]
# Recast the x and y points into usable format for cv2.fillPoly()
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
# Draw the lane onto the warped blank image
cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
cv2.polylines(color_warp, np.int32([pts_left]), isClosed=False, color=(255,0,255), thickness=15)
cv2.polylines(color_warp, np.int32([pts_right]), isClosed=False, color=(0,255,255), thickness=15)
# Warp the blank back to original image space using inverse perspective matrix (Minv)
newwarp = cv2.warpPerspective(color_warp, Minv, (w, h))
# Combine the result with the original image
result = cv2.addWeighted(new_img, 1, newwarp, 0.5, 0)
return result
# Method to determine radius of curvature and distance from lane center
# based on binary image, polynomial fit, and L and R lane pixel indices
# calc_curv_rad_and_center_dist(img_bin, l_line.best_fit, r_line.best_fit, l_lane_inds, r_lane_inds)
def calc_curv_rad_and_center_dist(bin_img, l_fit, r_fit, l_lane_inds, r_lane_inds):
# Define conversions in x and y from pixels space to meters
# ym_per_pix = 30/720 # meters per pixel in y dimension
# xm_per_pix = 3.7/700 # meters per pixel in x dimension
ym_per_pix = 16.0/720 # meters per pixel in y dimension
xm_per_pix = 3.7/1000 # meters per pixel in x dimension
left_curverad, right_curverad, center_dist = (0, 0, 0)
# Define y-value where we want radius of curvature
# I'll choose the maximum y-value, corresponding to the bottom of the image
h = bin_img.shape[0]
ploty = np.linspace(0, h-1, h)
y_eval = np.max(ploty)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = bin_img.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Again, extract left and right line pixel positions
leftx = nonzerox[l_lane_inds]
lefty = nonzeroy[l_lane_inds]
rightx = nonzerox[r_lane_inds]
righty = nonzeroy[r_lane_inds]
if len(leftx) != 0 and len(rightx) != 0:
# Fit new polynomials to x,y in world space
left_fit_cr = np.polyfit(lefty*ym_per_pix, leftx*xm_per_pix, 2)
right_fit_cr = np.polyfit(righty*ym_per_pix, rightx*xm_per_pix, 2)
# Calculate the new radii of curvature
left_curverad = ((1 + (2*left_fit_cr[0]*y_eval*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
right_curverad = ((1 + (2*right_fit_cr[0]*y_eval*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
# Distance from center is image x midpoint - mean of l_fit and r_fit intercepts
if r_fit is not None and l_fit is not None:
car_position = bin_img.shape[1]/2
l_fit_x_int = l_fit[0]*h**2 + l_fit[1]*h + l_fit[2]
r_fit_x_int = r_fit[0]*h**2 + r_fit[1]*h + r_fit[2]
lane_center_position = (r_fit_x_int + l_fit_x_int) /2
center_dist = (car_position - lane_center_position) * xm_per_pix
return left_curverad, right_curverad, center_dist
# Define method to fit polynomial to binary image based upon a previous fit (chronologically speaking);
# this assumes that the fit will not change significantly from one video frame to the next
def polyfit_using_prev_fit(binary_warped, left_fit_prev, right_fit_prev):
margin = 50
# Grab activated pixels
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
left_lane_inds = ((nonzerox > (left_fit_prev[0]*(nonzeroy**2) + left_fit_prev[1]*nonzeroy +
left_fit_prev[2] - margin)) & (nonzerox < (left_fit_prev[0]*(nonzeroy**2) +
left_fit_prev[1]*nonzeroy + left_fit_prev[2] + margin)))
right_lane_inds = ((nonzerox > (right_fit_prev[0]*(nonzeroy**2) + right_fit_prev[1]*nonzeroy +
right_fit_prev[2] - margin)) & (nonzerox < (right_fit_prev[0]*(nonzeroy**2) +
right_fit_prev[1]*nonzeroy + right_fit_prev[2] + margin)))
# Again, extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
left_fit_new, right_fit_new = (None, None)
if len(leftx) != 0:
# Fit a second order polynomial to each
left_fit_new = np.polyfit(lefty, leftx, 2)
if len(rightx) != 0:
right_fit_new = np.polyfit(righty, rightx, 2)
return left_fit_new, right_fit_new, left_lane_inds, right_lane_inds
# Define method to fit polynomial to binary image with lines extracted, using sliding window
def sliding_window_polyfit(binary_warped):
# Take a histogram of the bottom half of the image
histogram = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0) #WHC
# Create an output image to draw on and visualize the result
out_img = np.dstack((binary_warped, binary_warped, binary_warped))
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]//2) #W/2
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
# HYPERPARAMETERS
# Choose the number of sliding windows
nwindows = 9
# Set the width of the windows +/- margin
margin = 100
# Set minimum number of pixels found to recenter window
minpix = 50
# Set height of windows - based on nwindows above and image shape
window_height = np.int(binary_warped.shape[0]//nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0]) #H
nonzerox = np.array(nonzero[1]) #W
# Current positions to be updated later for each window in nwindows
leftx_current = leftx_base
rightx_current = rightx_base
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Rectangle data for visualization
rectangle_data = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
rectangle_data.append((win_y_low, win_y_high, win_xleft_low, win_xleft_high, win_xright_low, win_xright_high))
# Identify the nonzero pixels in x and y within the window #
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
left_fit, right_fit = (None, None)
# Fit a second order polynomial to each
if len(leftx) != 0:
left_fit = np.polyfit(lefty, leftx, 2)
if len(rightx) != 0:
right_fit = np.polyfit(righty, rightx, 2)
visualization_data = (rectangle_data, histogram)
return left_fit, right_fit, left_lane_inds, right_lane_inds, visualization_data
process project_video.mp4¶
In [29]:
l_line = Line()
r_line = Line()
video_output1 = 'project_video_output.mp4'
video_input1 = VideoFileClip('./project_video.mp4')#.subclip(0,10)
processed_video = video_input1.fl_image(process_image)
%time processed_video.write_videofile(video_output1, audio=False)
In [30]:
from IPython.display import HTML
HTML("""
<video width="640" height="360" controls>
<source src="{0}">
</video>
""".format('project_video_output.mp4'))
Out[30]:
process challenge_video.mp4¶
In [31]:
l_line = Line()
r_line = Line()
# video_output1 = 'project_video_output_diagnostic.mp4'
video_output1 = 'challenge_video_output.mp4'
video_input1 = VideoFileClip('./challenge_video.mp4')#.subclip(0,10)
processed_video = video_input1.fl_image(process_image)
%time processed_video.write_videofile(video_output1, audio=False)
In [32]:
from IPython.display import HTML
HTML("""
<video width="640" height="360" controls>
<source src="{0}">
</video>
""".format('challenge_video_output.mp4'))
Out[32]:
process harder_challenge_video.mp4¶
In [33]:
l_line = Line()
r_line = Line()
video_output1 = 'harder_challenge_video_output.mp4'
video_input1 = VideoFileClip('./harder_challenge_video.mp4').subclip(0,10)
processed_video = video_input1.fl_image(process_image)
%time processed_video.write_videofile(video_output1, audio=False)
In [34]:
from IPython.display import HTML
HTML("""
<video width="640" height="360" controls>
<source src="{0}">
</video>
""".format('harder_challenge_video_output.mp4'))
Out[34]: